Predictive Analytics For Dummies will help the reader understand the core of predictive analytics and get them started quickly as possible with readily available tools to collect and analyze data, and then make predictions. This book will not bog the reader down with advanced mathematical pre-requisites, but will cover just enough concepts to make meaningful decisions on which algorithms to use and how to create effective predictive models. The author will also address "soft" issues, including handling people, setting realistic goals, protecting budgets, making useful presentations, and more, to help the reader prepare for shepherding predictive analysis projects through their companies

About the Author

Dr. Anasse Bari (Washington DC) has over five years of large-scale software architecture experience in designing and implementing software systems under different platforms. He is a Fulbright scholar, a software engineer, and a data mining expert. Dr. Bari works for the World Bank Group in Washington DC. Mohamed Chaouchi (Bethesda, MD) has over 12 years of software engineering experience in both the public and private sectors. His expertise spans various fields in information technology namely service orientated architecture and web services. Mohamed has conducted extensive research using data mining methods in both health and financial domains. Tommy Jung (Belmont, CA) is a software engineer who has been developing software applications for over 12 years. He has worked extensively on creating analytic's tools for natural language processing in his days at a speech recognition company and then turned his focus to the web analytics, database marketing, and stock analysis

Table of Contents:
Part I: Getting Started with Predictive Analytics Chapter 1: Entering the Arena Chapter 2: Predictive Analytics in the Wild Chapter 3: Exploring Your Data Types and Associated Techniques Chapter 4: Complexities of Data Part II: Incorporating Algorithms in Your Models Chapter 5: Applying Models Chapter 6: Identifying Similarities in Data Chapter 7: Predicting the Future Using Data Classification Part III: Developing a Roadmap Chapter 8: Convincing Your Management to Adopt Predictive Analytics 1 Chapter 9: Preparing Data Chapter 10: Building a Predictive Model Chapter 11: Visualization of Analytical Results Part IV: Programming Predictive Analytics Chapter 12: Creating Basic Prediction Examples Chapter 13: Creating Basic Examples of Unsupervised Predictions Chapter 14: Predictive Modeling with R Chapter 15: Avoiding Analysis Traps Chapter 16: Targeting Big Data Part V: The Part of Tens Chapter 17: Ten Reasons to Implement Predictive Analytics Chapter 18: Ten Steps to Build a Predictive Analytic Model Index